Electronic device and method for changing chatbot

ABSTRACT

An artificial intelligence (AI) system which utilizes machine learning algorithm such as deep learning and application is provided. The artificial intelligence (AI) system includes a controlling method of an electronic device for determining a chatbot using an artificial intelligence learning model includes receiving a voice uttered by a user, processing the voice and acquiring text information corresponding to the voice, and displaying the text information on a chat screen, determining a chatbot for providing a response message regarding the voice by inputting the acquired text information and chat history information regarding the chat screen to a model which is trained to determine the chatbot by inputting text information and chat history information, transmitting the acquired text information and the chat history information regarding the chat screen to a server for providing the determined chatbot, and receiving a response message from the server and displaying the response message on the chat screen.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. §119(a) of a Korean patent application number 10-2017-0154939, filed onNov. 20, 2017, in the Korean Intellectual Property Office, thedisclosure of which is incorporated by reference herein in its entirety.

BACKGROUND 1. Field

The disclosure relates to an electronic device and a control methodthereof More particularly, the disclosure relates to an electronicdevice and a control method thereof that can determine a chatbot basedon text information corresponding to voice and a chat history on a chatscreen.

In addition, the disclosure relates to an artificial intelligence (AI)system and an application thereof that simulates functions such asrecognition and judgment of a human brain using a machine learningalgorithm

2. Description of Related Art

In recent years, artificial intelligence systems that implement humanintelligence have been used in various fields. Artificial intelligence(AI) system is a system that the machine learns, judges and becomessmart, unlike the existing rule-based smart system. As the use ofartificial intelligence systems improves recognition rate andunderstanding of user's taste more accurately, existing rule-based smartsystems are gradually being replaced by deep learning-based artificialintelligence systems.

AI technology is composed of machine learning (e.g., deep learning) andelementary technologies which utilizes machine learning.

Machine learning is an algorithm technology that classifies and/orlearns the characteristics of input data by itself. Element technologyis a technology that simulates functions such as recognition andjudgment of human brain using machine learning algorithms such as deeplearning. Machine learning is composed of technical fields such aslinguistic understanding, visual understanding, reasoning and/orprediction, knowledge representation, motion control, and the like.

Various fields in which AI technology is applied are as follows.Linguistic understanding is a technology for recognizing, applyingand/or processing human language and/or characters and includes naturallanguage processing, machine translation, dialogue system, question andanswer, speech recognition and/or synthesis, and the like. Visualunderstanding is a technique for recognizing and processing objects ashuman vision, including object recognition, object tracking, imagesearch, human recognition, scene understanding, spatial understanding,image enhancement, and the like. Inference prediction is a technique forjudging and logically inferring and predicting information, includingknowledge and/or probability based inference, optimization prediction,preference-based planning, and recommendation. Knowledge representationis a technology for automating human experience information intoknowledge data, including knowledge building (data generation and/orclassification) and knowledge management (data utilization). The motioncontrol is a technique for controlling the autonomous running of avehicle and a motion of a robot, including motion control (navigation,collision, driving), operation control (behavior control), and the like.

In recent years, due to the development of artificial intelligence, achat can be performed with a chatbot (i.e., a chatting robot) providedby an external server. The chatbot has been developed for a specificpurpose and can provide a chat service only for the developed purpose(e.g., shopping, customer consultation, reservation, and the like).

If there is a need to perform chatting for a different purpose during achat with these chatbots, one needs to stop chatting with the chatbotcurrently performing the conversation and start chatting with thechatbot with other purposes. In this case, there is a drawback that auser has to decide the chatbot that he or she intends to performconversation, and that a new chat should be performed regardless of theexisting chat history.

The above information is presented as background information only toassist with an understanding of the disclosure. No determination hasbeen made, and no assertion is made, as to whether any of the abovemight be applicable as prior art with regard to the disclosure.

SUMMARY

Aspects of the disclosure are to address at least the above-mentionedproblems and/or disadvantages and to provide at least the advantagesdescribed below. Accordingly, an aspect of the disclosure is to anelectronic device which determines a chatbot based on the acquired textinformation and chat history information and is capable of performingchatting without interruption of conversation flow by chatting textinformation and chat history information to the determined chatbot, anda controlling method thereof.

Another aspect of the disclosure is to provide a controlling method ofan electronic device for determining a chatbot using an artificialintelligence learning model includes receiving a voice uttered by auser, processing the voice and acquiring text information correspondingto the voice, and displaying the text information on a chat screen,determining a chatbot for providing a response message regarding thevoice by inputting the acquired text information and chat historyinformation regarding the chat screen to a model which is trained todetermine the chatbot by inputting text information and chat historyinformation, transmitting the acquired text information and the chathistory information regarding the chat screen to a server for providingthe determined chatbot, and receiving a response message from the serverand displaying the response message on the chat screen.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments.

In accordance with an aspect of the disclosure, an electronic device isprovided. The electronic device includes a display, a user inputter, acommunicator, a processor electrically connected to the display, theuser inputter, and the communicator, and a memory electrically connectedto the processor, wherein the processor is configured to: control thedisplay to display a chat screen, when voice uttered by a user is inputthrough the user inputter, acquire text information corresponding to thevoice by processing the voice, control the display to display the textinformation on the chat screen, determine a chatbot for providing aresponse message regarding the voice by inputting the acquired textinformation and chat history information regarding the chat screen to amodel which is trained to determine the chatbot by inputting textinformation and chat history information, control the communicator totransmit the acquired text information and the chat history informationregarding the chat screen to a server for providing the determinedchatbot, and control the display to receive a response message from theserver and display the response message on the chat screen.

As described above, a user may reduce troublesome efforts to designate achatbot one by one, and even if a chatbot is changed, a user may performconversation with a changed chatbot without the flow of a conversationbeing interrupted.

Other aspects, advantages, and salient features of the disclosure willbecome apparent to those skilled in the art from the following detaileddescription, which, taken in conjunction with the annexed drawings,discloses various embodiments of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainembodiments of the disclosure will be more apparent from the followingdescription taken in conjunction with the accompanying drawings, inwhich:

FIGS. 1A and 1B are views to describe an embodiment of chatting with achatbot by determining the chatbot based on text information and chathistory information according to an embodiment of the disclosure;

FIG. 2 is a view briefly illustrating system including an electronicdevice and a chatbot server providing a chatbot according to anembodiment of the disclosure;

FIGS. 3A and 3B are block diagrams illustrating a configuration of anelectronic device according to various embodiments of the disclosure;

FIG. 4 is a block diagram illustrating a plurality of modules includedin an electronic device according to an embodiment of the disclosure;

FIG. 5 is a flowchart describing a method for registering a chatbotaccording to an embodiment of the disclosure;

FIG. 6A is a view to describe structure information of a function whicha chatbot provides according to an embodiment of the disclosure;

FIG. 6B is a view to describe a method for identifying structureinformation of a chatbot according to an embodiment of the disclosure;

FIG. 7 is a flowchart to describe an embodiment of performing chattingby changing a chatbot according to an embodiment of the disclosure;

FIGS. 8, 9A, and 9B are views to describe embodiments of transmitting aninquiry message and receiving a response message with a chatbot serveraccording to various embodiments of the disclosure,

FIG. 10 is a flowchart to describe a controlling method of an electronicdevice according to an embodiment of the disclosure;

FIG. 11 is a view briefly illustrating a system including an electronicdevice, a server providing a personal assistant chatbot and an externalchatbot server according to another embodiment of the disclosure;

FIG. 12 is a flowchart to describe an embodiment of chanting a chatbotby a personal assistant chatbot according to another embodiment of thedisclosure;

FIG. 13 is a block diagram illustrating a configuration of an electronicdevice to learn and use a model for determining a chatbot according toan embodiment of the disclosure;

FIGS. 14A and 14B are block diagrams illustrating a specificconfiguration of a learning unit and a determination unit according toan embodiment of the disclosure; and

FIG. 15 is a flowchart of network system using a chatbot determinationmodel according to various embodiments of the disclosure.

Throughout the drawings, like reference numerals will be understood torefer to like parts, components, and structures.

DETAILED DESCRIPTION

The following description with reference to the accompanying drawings isprovided to assist in a comprehensive understanding of variousembodiments of the disclosure as defined by the claims and theirequivalents. It includes various specific details to assist in thatunderstanding but these are to be regarded as merely exemplary.Accordingly, those of ordinary skill in the art will recognize thatvarious changes and modifications of the various embodiments describedherein can be made without departing from the scope and spirit of thedisclosure. In addition, descriptions of well-known functions andconstructions may be omitted for clarity and conciseness.

The terms and words used in the following description and claims are notlimited to the bibliographical meanings, but, are merely used by theinventor to enable a clear and consistent understanding of thedisclosure. Accordingly, it should be apparent to those skilled in theart that the following description of various embodiments of thedisclosure is provided for illustration purpose only and not for thepurpose of limiting the disclosure as defined by the appended claims andtheir equivalents.

It is to be understood that the singular forms “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.Thus, for example, reference to “a component surface” includes referenceto one or more of such surfaces.

In this document, the expressions “have,” “may have,” “including,” or“may include” may be used to denote the presence of a feature (e.g., anumerical value, a function, an operation), and does not exclude thepresence of additional features.

In this document, the expressions “A or B,” “at least one of A and/orB,” or “one or more of A and/or B,” and the like include all possiblecombinations of the listed items. For example, “A or B,” “at least oneof A and B,” or “at least one of A or B” includes (1) at least one A,(2) at least one B, (3) at least one A and at least one B all together.

The terms such as “first,” “second,” and so on may be used to describe avariety of elements, but the elements should not be limited by theseterms. The terms are used only for the purpose of distinguishing oneelement from another.

It is to be understood that a component (e.g., a first component) is“operatively or communicatively coupled with/to” another component(e.g., a second component) is that any such element may be directlyconnected to the other element or may be connected via another element(e.g., a third element). On the other hand, when it is mentioned that anelement (e.g., a first element) is “directly connected” or “directlyaccessed” to another element (e.g., a second element), it can beunderstood that there is no other component (e.g. a third component)between the other components.

The expression “configured to” can be used interchangeably with, forexample, “suitable for”, “having the capacity to”, “designed to”,“adapted to”, “made to”, or “capable of”. The expression “configured to”does not necessarily mean “specifically designed to” in a hardwaresense. Instead, under some circumstances, “a device configured to” mayindicate that such a device can perform an operation along with anotherdevice or part. For example, the expression “a processor configured toperform A, B, and C” may indicate an exclusive processor (e.g., anembedded processor) to perform the corresponding operation, or ageneric-purpose processor (e.g., a central processor (CPU) orapplication processor (AP)) that can perform the correspondingoperations by executing one or more software programs stored in thememory device.

An electronic apparatus and an external device in accordance withvarious embodiments of the disclosure may include at least one of, forexample, smartphones, tablet PCs, mobile phones, video telephones,electronic book readers, desktop PCs, laptop PCs, netbook computers,workstations, servers, a personal digital assistant (PDA), a portablemultimedia player (PMP), an moving picture experts group phase 1 orphase 2 (MPEG-1 or MPEG-2) audio layer 3 (MP3) player, a medical device,a camera, or a wearable device. A wearable device may include at leastone of the accessory type (e.g.: as a watch, a ring, a bracelet, abracelet, a necklace, a pair of glasses, a contact lens or ahead-mounted-device (HMD)); a fabric or a garment-embedded type (e.g.: askin pad or a tattoo); or a bio-implantable circuit. In someembodiments, the electronic apparatus may be, for example, a television,a digital versatile disc (DVD) player, audio, refrigerator, cleaner,ovens, microwaves, washing machines, air purifiers, set top boxes, homeautomation control panels, security control panels, media box (e.g.:Samsung HomeSync™, Apple TV™, or Google TV™), game consoles (e.g.: Xbox™PlayStation™), electronic dictionary, electronic key, camcorder, orelectronic frame.

In other embodiments, the electronic apparatus and the external devicemay include at least one of a variety of medical devices (e.g.: variousportable medical measurement devices such as a blood glucose meter, aheart rate meter, a blood pressure meter, or a temperature measuringdevice), magnetic resonance angiography (MRA), magnetic resonanceimaging (MRI), computed tomography (CT), or ultrasonic wave device, andthe like), navigation system, global navigation satellite system (GNSS),event data recorder (EDR), flight data recorder (FDR), automotiveinfotainment devices, marine electronic equipment (e.g.: marinenavigation devices, gyro compasses, and the like), avionics, securitydevices, car head units, industrial or domestic robots, drone, ATMs,points of sale (POS) of stores, or IoT devices (e.g.: light bulbs,sensors, sprinkler devices, fire alarms, thermostats, street lights,toasters, exercise equipment, hot water tanks, heater, boiler, and thelike).

In this disclosure, the term user may refer to a person who uses anelectronic apparatus or an apparatus (e.g.: artificial intelligenceelectronic apparatus) which uses an electronic apparatus.

Hereafter, the disclosure will be further described with reference toattached drawings.

FIGS. 1A and 1B are views to describe an embodiment of chatting with achatting robot (hereinafter “chatbot”) by determining the chatbot basedon text information and chat history information according to anembodiment of the disclosure.

Referring to FIGS. 1A and 1B, an electronic device 100 can execute thepersonal assistant program according to a predetermined user input. Thepreset user input may be a user input for selecting an iconcorresponding to a personal assistant program displayed on the displayscreen, but this is only an embodiment, and can be implemented as auser's voice including a preset word (e.g., Bixby), and a user input forselecting a button provided in a predetermined area of the electronicdevice 100, and the like. The personal assistant program is a programfor executing a personal assistant chatbot to provide a personalassistant service to a user using the electronic device 100 and may bestored in the electronic device 100, but this is merely exemplary andcan be stored in a separate personal assistant chatbot server.

When the personal assistant program is executed, the electronic device100 can display a chat screen capable of chatting with the personalassistant chatbot. The chat screen may be a dedicated chat screen forchatting with a personal assistant chatbot or a chatbot provided by anexternal server, but this is merely an example, and can be a chat screenprovided by a general chatting program for chatting with another user.

While the chat screen is being displayed, the electronic device 100 canreceive the user's voice uttered by the user through a microphone. Theelectronic device 100 can acquire text information corresponding to theuser's voice acquired through the microphone. Specifically, theelectronic device 100 can transmit the user's voice acquired through themicrophone to an external server (e.g., a Speech to text (STT) server)and acquire text information corresponding to the user's voice.Alternatively, the electronic device 100 may acquire text informationcorresponding to a user's voice using the STT program stored therein. Inthe above-described embodiment, it is described that text information isacquired through the user's voice uttered by the user. However, this ismerely exemplary, and text information can be acquired through a keypaddisplayed on the display, and text information can be acquired throughan external input device (e.g., a keyboard).

The electronic device 100 may display the acquired text information onthe chat screen. For example, if the text information corresponding tothe user's voice is “Galaxy S6 touch is not working well sometimes”, theelectronic device 100 may display on the chat screen “My Galaxy S6 touchis not working well sometimes.” The electronic device 100 can display anicon (U) indicating that the user has uttered “My Galaxy S6 touch is notworking well sometimes” around the text as illustrated panel (a) of FIG.1A.

The electronic device 100 may determine whether the acquired textinformation is text information that the personal assistant chatbot canrespond to. The electronic device 100 may determine whether the acquiredtext information is text information for performing a function withinthe electronic device 100 or text information for searching informationstored in the electronic device 100 and determine whether the acquiredtext information is text information that the personal assistant chatbotcan respond to.

If the text information is that the personal assistant chatbot canrespond to, the electronic device 100 may provide a response message tothe text information via the personal assistant chatbot. If the textinformation is that the personal assistant chatbot cannot respond to,the electronic device 100 may determine a chatbot capable of providing aresponse message to the text information. The electronic device 100 candetermine the chatbot that can provide a response message to the textinformation by inputting the text information into a learned model todetermine the chatbot based on the text information and the chat historyinformation. For example, the electronic device 100 may determine a“customer consultation chatbot” as a chatbot that can provide a responsemessage to the user's voice via the learned model. The chatbot is achatbot having a chat domain corresponding to the user text information,and can be provided by an external server.

When a chatbot capable of providing a response message to the text isdetermined through the learned model, the electronic device 100 maydisplay an inquiry message for determining whether to request a responsemessage to the user through the personal assistant chatbot. For example,as shown in panel (b) of FIG. 1A, the electronic device 100 may displayan inquiry message such as “Shall I ask, ‘Galaxy S6 touch is not workingsometimes’ to the Samsung Service?” Around the inquiry message, an icon(A) indicating that the message is uttered by the personal assistantchatbot can be displayed.

When a user's voice for a response to the inquiry message is input, theelectronic device 100 can acquire text information on the user's voiceand display the obtained text information. For example, as shown inpanel (c) of FIG. 1A, the electronic device 100 may display textinformation “Yes.”

When the request response to the inquiry message is input, theelectronic device 100 may transmit the previously acquired textinformation to the server providing the determined chatbot. For example,the electronic device 100 may transmit text information such as “GalaxyS6 touch is not working sometimes” to a customer consultation chatbotserver that provides a customer consultation chatbot.

The customer consultation chatbot server can generate a response messagefor the received text information using the customer consultationchatbot and transmit the generated response message to the electronicdevice 100. The electronic device 100 may display the received responsemessage. For example, the electronic device 100 may display a responsemessage “Hello, this is a Samsung service, please try to change touchvia touch sensitivity settings” as shown in panel (d) of FIG. 1A. Aroundthe response message, an icon (C) indicating that the customerinformation chatbot uttered can be displayed.

As described above, the electronic device 100 may determine a chatbotcapable of providing a response message for a user's voice through thepersonal assistant chatbot, and receive a response message from thedetermined chatbot.

The electronic device 100 may receive a user's voice which is newlyuttered by a user through a microphone. The electronic device 100 mayacquire text information which corresponds to a user's voice newlyacquired through the microphone.

The electronic device 100 may display the acquired text information onthe chat screen. For example, if the text information corresponding tothe user's voice is “Still it is not working. It would be better to buya new one. How much will it cost to buy Galaxy now”, the electronicdevice 100, as illustrated in panel (a) of FIG. 1B, may display “Stillit is not working. It would be better to buy a new one. How much will itcost to buy Galaxy now” on the chat screen.

The electronic device 100 may determine whether the obtained textinformation is text information that the personal assistant chatbot canrespond to. If the information is that the personal assistant chatbotcannot respond to, the electronic device 100 may determine a chatbotcapable of providing a response message regarding the text information.The electronic device 100 can determine a chatbot that can input textinformation and chat history information into the learned model toprovide a response message regarding the text information. The chathistory information may be information on a conversation history betweenthe user and the chatbot in the current or previous chat screen. Forexample, the electronic device 100 may determine a “shopping chatbot” asa chatbot that can provide a response message to the user's voicethrough the learned model. That is, the electronic device 100 candetermine a chatbot that can provide a response message to a user'svoice by using not only text information corresponding to a simpleuser's voice but also chat history information.

When the chatbot that can provide a response message regarding a textthrough the learned model is determined, the electronic device 100 maydisplay an inquiry message to determine whether to ask a responsemessage to the chatbot through the personal assistant chatbot.

When user's voice for responding to the inquiry message is input, theelectronic device 100 may acquire text information regarding the user'svoice and display the acquired text information. For example, asillustrated in panel (c) of FIG. 1B, the electronic device 100 maydisplay text information “Yes.”

When the request response to the inquiry message is input, theelectronic device 100 can transmit the previously obtained textinformation to the determined chatbot. For example, the electronicdevice 100 may transmit the text information “Would you like to inquireabout the price of a new smartphone that a user of Galaxy S6 would liketo buy” in panel (b) of FIG. 1B and chat history information “Galaxy S6touch is not working” together to a shopping chatbot server providingthe shopping chatbot.

The shopping chatbot server can generate a response message to thereceived text information and chat history information by using thecustomer consultation chatbot. The shopping chatbot can generate aresponse message using both the text information and the chat historyinformation together. For example, a shopping chatbot can generate aresponse message by selecting a smartphone that has a good touch amongrecommended smartphones for a user of Galaxy S6.

The shopping chatbot server can transmit the generated response messageto the electronic device 100. The electronic device 100 may display thereceived response message. For example, the electronic device 100 may,as illustrated in panel (d) of FIG. 1B, display a response messagesaying “Hi, I'm a shopping helper. Galaxy S8 has two product options:which option do you like to find out of 1. Galaxy S8−800,000 Won and 2.Galaxy S8+900,000 Won?” An icon (S) indicating that the message isuttered by the shopping chatbot can be displayed around the responsemessage.

As described above, the electronic device 100 may determine a chatbotcapable of providing a response message by taking into consideration ofnot only the user's voice but also the chat history information throughthe personal assistant chatbot, and receive a response message from thedetermined chatbot.

In the above-described embodiment, the electronic device 100 determinesthat the chatbot by inputting the text information and the chat historyinformation into the learned model. However, this is merely exemplary,and a chatbot which can input not only text information but also variouscontext information to the learned model to provide a response messagecan be determined. The context information may include user profileinformation, user search information, user preference information, andthe like. The user profile information may be user information stored bythe user in the electronic device 100 or the personal assistant program,and may include various user information such as user's gender, user'sage, user's body information, user's origin information, and the like.The user search information may include information that the user hassearched before executing the personal assistant chatbot, and the userpreference information may include information on the user's preferredcontent and a category.

Also, the electronic device 100 can transmit various context informationas well as text information and chat history information to a chatbotserver that provides chatbots. The chatbot server may generate aresponse message based on text information and various contextinformation. For example, the shopping chatbot server may provide aresponse message by selecting a recommended product based on userprofile information, user search information, user preferenceinformation, and the like, along with text information.

The learned model as described above is a determination model which islearned based on artificial knowledge and may be a model based on neuralnetwork. The object determination model can be designed so that humanbrain structure can be simulated in computer, and may include aplurality of network nodes which simulate neurons of human neuralnetwork and have a weight. The plurality of network nodes may form eachconnection relation so that neurons simulate synaptic activities ofneurons that send and receive a signal through synapse. In addition, theobject determination model may include, for example, a neural networkmodel or a deep learning model that is developed from the neural networkmodel. In the deep learning model, the plurality of network nodes arepositioned in different depths (or layers) and may transceive dataaccording to a convolution connection relation. An example of an objectdetermination model may include deep neural network (DNN), recurrentneural network (RNN), bidirectional recurrent deep neural network(BRDNN), but it is not limited thereto.

Also, the electronic device 100 can use a personal assistant program,which is an artificial intelligence agent, to determine a chatbot toprovide a response message as described above and to provide a responsemessage. The personal assistant program is a dedicated program forproviding an AI-based service and is executed by a general-purposeprocessor (e.g., a CPU) or a separate AI-specific processor (e.g., aGPU). In particular, an artificial intelligence agent can controlvarious modules to be described later.

Specifically, a predetermined user input (e.g., an icon touchcorresponding to a personal assistant chatbot, a user's voice includinga predetermined word, or the like) is input or a button (e.g., a buttonfor executing an artificial intelligence agent) is pressed, theartificial intelligence agent can operate. The artificial intelligentagent determines a chatbot capable of responding to the user's voicebased on the text information and the chat history informationcorresponding to the input user's voice, and transmits the textinformation and chat history information to the chatbot server providingthe determined chatbot. Then, the artificial intelligence agent candisplay the response message received from the chatbot server.

Of course, if the predetermined user input is detected on the screen ora button (e.g., a button for executing the AI agent) of the electronicdevice 100 is pressed, the AI agent may operate. Alternatively, theartificial intelligence agent may be in a previously executed state inwhich a predetermined user input is sensed or a button provided in theelectronic device 100 is selected. In this case, after predetermineduser input is detected or a button provided on the electronic device 100is selected, the artificial intelligent agent of the electronic device100 can determine a chatbot that can respond to the user's voice. Inaddition, the artificial intelligence agent may be in a standby statepreviously selected when a preset user input is sensed or a buttonprovided in the electronic device 100 is selected. The standby state isa state in which a predefined user input is received to control thestart of operation of the AI agent. If a predefined user input is sensedwhile the artificial intelligence agent is in the standby state, or abutton provided on the electronic device 100 is selected, the electronicdevice 100 may activate the artificial intelligence agent and decide achatbot capable of responding to a user's voice.

The artificial agent may control various modules to be described later.This will be described in a greater detail.

FIG. 2 is a view briefly illustrating system including an electronicdevice and a chatbot server providing a chatbot according to anembodiment of the disclosure.

Referring to FIG. 2, the system may include the electronic device 100and a plurality of chatbot servers 200-1, 200-2, . . . , 200-n.

The electronic device 100 may store a personal assistant program. A usermay perform chatting with a personal assistant chatbot through apersonal assistant program or a chatbot provided by an external chatbotserver. When a predetermined user command is input, the electronicdevice 100 may execute or activate a personal assistant program,

Also, when an event for registering a new chatbot (e.g., an event forreceiving information about a new chatbot from the outside, an event forinputting a user command for adding a new chatbot, and the like) isgenerated, the electronic device 100 can register a new chatbot. Theelectronic device 100 can acquire the structure information of the newchatbot. For example, the electronic device 100 can acquire thestructure information of the new chatbot using the metadata of the newchatbot, and can acquire the structure information of the new chatbotthrough the conversation with the new chatbot. This will be describedlater in detail.

After the personal assistant program is executed, when the user's voiceis input, the electronic device 100 may input the text information andthe chat history corresponding to the user's voice to the learned modeland determine a chatbot for providing a response message regarding theuser's voice from among a plurality of chatbot servers 200-1, 200-2, . .. , 200-n. In addition, the electronic device 100 may provide a responsemessage regarding the user's voice through the personal assistantprogram. In particular, while the electronic device 100 performschatting with a first chatbot, when a chatbot for providing a responsemessage regarding the current user's voice is determined as a secondchatbot, the electronic device 100 may provide the text information andchat history information corresponding to the user's voice to the secondchatbot server providing the second chatbot.

Also, the electronic device 100 may transmit text information and chathistory information about the user's voice to the chatbot server, andmay receive and provide a response message to the text information andchat history information from the chatbot server. The electronic device100 may generate the inquiry message including the text information andthe chat history information by using the structure information of thechatbot. Also, the chatbot server 200 can transmit a response messageregarding the inquiry message using the structure information.

The plurality of chatbot servers 2004, 200-2, . . . , 200-n can providechatbots having corresponding chat domains. For example, a first chatbotserver 200-1 may provide a chatbot having a shopping domain, a secondchatbot server 200-2 may provide a chatbot having an IoT device controldomain, and a third chatbot server 200-3 may provide a chatbot having acustomer consultation domain, but is not limited thereto.

The plurality of chatbot servers 2004 200-2, . . . , 200-n may generatea response message based on the text information and chat historyinformation received by the personal assistant program of the electronicdevice 100. Each of the plurality of chatbot servers 200-1, 200-2, . . ., 200-n may perform natural language processing on the received textinformation, and generate a response message based on the textinformation and the chat history information processed as the naturallanguage.

The plurality of chatbot servers 200-1, 200-2, . . . , 200-n cangenerate a response message based on the structure information of thechatbot. That is, the plurality of chatbot servers 200-1, 200-2, . . . ,200-n can generate a response message based on functions, sub-functions,attributes, and the like that the chatbot can provide.

Further, between the personal assistant program of the electronic device100 and the plurality of chatbot servers 200-1, 200-2, . . . , 200-n,there may be information transfer specification for transmitting textinformation and chat history information (or context information). Theinformation transfer specification is specification for transmittinginformation between the personal assistant program and the chatbotserver 200-n, and may provide an inquiry message and a response messageusing structure information of a chatbot.

Therefore, the personal assistant program of the electronic device 100may transmit text information and chat history information based oninformation transfer specification.

FIGS. 3A and 3B are block diagrams showing the configuration of anelectronic device according to an embodiment of the disclosure.

Referring to FIG. 3A, the electronic device 100 may include a display110, a communicator 120, a user inputter 30, a memory 140, and aprocessor 150. The configurations shown in FIG. 3A are diagrams forimplementing embodiments of the disclosure, and appropriate hardwareand/or software configurations of a level obvious to those skilled inthe art may be further included in the electronic device 100.

The display 110 may provide various screens. In particular, the display110 may display a chat screen for chatting with a personal assistantchatbot or an external chatbot. The chat screen may include an icon,text, or the like for indicating a user, a personal assistant chatbot,or an external chatbot.

The communicator 120 can perform communication with an external devicethrough various communication methods. In particular, the communicator120 can communicate with an external chat server and receive a responsemessage. Also, if there is a personal assistant chatbot server, thecommunicator 120 can communicate with the personal assistant chatbotserver.

The user inputter 130 may receive various user inputs and transmit thevarious user inputs to the processor 150. In particular, the userinputter 130 may include a touch sensor, a (digital) pen sensor, apressure sensor, a key, or a microphone. The touch sensor can use, forexample, at least one of an electrostatic type, a pressure sensitivetype, an infrared type, and an ultrasonic type. The (digital) pen sensormay be, for example, a part of a touch panel or may include a separaterecognition sheet. The key may include, for example, a physical button,an optical key, or a keypad. The microphone may be provided inside theelectronic device 100 for receiving the user's voice but may be providedoutside the electronic device 100 to be electrically connected to theelectronic device 100.

In particular, the user inputter 130 may acquire an input signalaccording to a user input for selecting a predetermined user touch forselecting an icon corresponding to the personal assistant program or abutton provided outside the electronic device 100. The user inputter 130may then send an input signal to the processor 150.

The memory 140 may store instructions or data related to at least oneother component of the electronic device 100. In particular, the memory140 may be implemented as a non-transitory memory, a transitory memory,a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).The memory 140 is accessed by the processor 150 andread/write/modify/delete/update of data by the processor 150 can beperformed. The term memory in the disclosure includes the memory 140,ROM (not shown), RAM (not shown) in the processor 150, or a memory card(not shown) (e.g., a micro SD card and a memory stick) mounted in theelectronic device 100. In addition, the memory 140 may store a programand data for configuring various screens to be displayed in the displayarea of the display 110.

In particular, the memory 140 may store a personal assistant program.The personal assistant program is a personalized program for providingvarious services for the electronic device 100. In particular, thepersonal assistant program may include a chatbot registration module 410and a chatbot determination module 420, as shown in FIG. 4. This will bedescribed later in detail with reference to FIG. 4

The memory 140 may store the chatbot determination model 430 which islearned to determine a chatbot for providing a response message by usingthe text information and chat history information (or contextinformation).

The processor 150 may be electrically connected to the display 110, thecommunicator 120, the user inputter 130, and the memory 140 to controlthe overall operation and functions of the electronic device 100. Inparticular, the processor 150 may provide a chat service with thechatbot using various modules stored in the memory 140. In particular,the processor 150 may control the display 110 to display a chat screenfor chatting with the chatbot. When a voice uttered by the user is inputthrough the user inputter 130 while the chat screen is displayed, theelectronic device 100 processes the user's voice to acquire textinformation corresponding to the voice, and control the display 110 todisplay text information on the chat screen. The processor 150 maydetermine a chatbot for inputting text information and chat historyinformation regarding a chat screen to the model learned to determine achatbot by inputting text information and history information, andcontrol the communicator 120 to transmit the obtained text informationto the chatbot server providing the determined chatbot and chat historyinformation regarding the chat screen. The processor 150 may control thedisplay 110 to receive a response message from the chatbot serverdetermined through the communicator 120 and display a response messageon a chat screen. A method of providing a response message of a chatbotby the processor 150 will be described later.

Referring to FIG. 3B, the electronic device 100 may include the display110, the communicator 120, the user inputter 130, the memory 140, theprocessor 150, the camera 160, and an audio outputter 170. Since thedisplay 110, the memory 140, and the user inputter 130 have beendescribed with reference to FIG. 3A, redundant descriptions will beomitted.

The communicator 120 can perform communication with various types ofexternal devices according to various types of communication methods.The communicator 120 may include at least one of a Wi-Fi chip 121, aBluetooth chip 122, and a wireless communication chip 123. The processor150 can communicate with an external chat server or various externaldevices using the communicator 120. In addition, the communicator 120can perform communication with an external device through variouscommunication chips such as an NFC chip.

The camera 160 can take an image including an external object. Thecamera 160 may be provided on at least one of the front and rear of theelectronic device 100. The camera 160 may be provided inside theelectronic device 100, but it is merely exemplary, and the camera existsoutside the electronic device 100, and may be wired or wirelesslyconnected to the electronic device 100.

The audio outputter 170 is configured to output various kinds of audiodata as well as various kinds of notification sounds and voice messagesin which various processing operations such as decoding, amplification,and noise filtering are performed by an audio processor (not shown). Inparticular, the audio outputter 170 may be implemented as a speaker, butthis is merely exemplary and it may be implemented as an output terminalcapable of outputting audio data.

In particular, the audio outputter 170 may provide a user withinformation regarding a search result to a user in an audio format.

The processor 150 (or controller) may control overall operations of theelectronic device 100 using various programs stored in the memory 140.

The processor 150 may be composed of a RAM 151, a ROM 152, a graphicsprocesser 153, a main CPU 154, first to n^(th) interfaces 155-1 to155-n, and a bus 156. The RAM 151, the ROM 152, the graphics processor153, the main CPU 154, the first to n^(th) interfaces 155-1 to 155-n andthe like can be connected to each other via the bus 156.

FIG. 4 is a block diagram illustrating a plurality of modules includedin an electronic device according to an embodiment of the disclosure.

Referring to FIG. 4, the electronic device 100 may include a chatbotregistration module 410 and a chatbot determination module 420.

The chatbot registration module 410 is a module to register a chatbot toa personal assistant program, and may include a chatbot metadataprocessing module 411, a chatbot analysis module 413, and a chatbotconnection management module 415.

The chatbot metadata processing module 411 can acquire and register thestructure information of the chatbot based on the metadata of thechatbot when the chatbot exists. Specifically, the chatbot metadataprocessing module 411 analyzes the metadata of the chatbot and obtainsand stores the structure information on the attributes for performingthe functions and functions provided by the chatbot.

If the metadata of the chatbot does not exist, the chatbot analysismodule 413 can analyze and register the structure information of thechatbot through a chat with the chatbot. Specifically, the chatbotanalysis module 413 can transmit an inquiry message for inquiring thestructure information of the functions provided by the chatbot to thechatbot server that provides the chatbot, receive the response messageto the inquiry message, and generate the structure information of thefunctions provided by the chatbot. This will be described later indetail with reference to FIG. 6B.

Particularly the chatbot analysis module 413 may include a textprocessing module 413-1, a function extraction module 413-2, aconversation flow management module 413-3, and an attribute extractionmodule 413-4. The text processing module 413-1 can perform textprocessing of the response message received from the chatbot. Thefunction extracting module 413-2 and the attribute extracting module413-4 can extract the functions and attributes of the chatbot based onthe text information text-processed from the response message. Theconversation flow management module 413-3 can provide the inquirymessage necessary for conversation flow to extract the functions andattributes of the chatbot.

The chatbot connection management module 415 can manage variousinformation for connection with the chatbot server. For example, thechatbot connection management module 415 may store information on achatbot provided by the chatbot server, address information of thechatbot server, identification information, domain information, and thelike.

The information of the chatbot and structure information regarding thechatbot registered through the chatbot registration module 410 may beprovided to the chatbot determination model 430.

The chatbot determination module 420 can determine a chatbot accordingto a user's voice. Particularly, the chatbot determination module 420can determine the chatbot that provides the response message to the uservoice by inputting the text information corresponding to the user voiceand the context information (including the chat history information) tothe chatbot determination model 430.

in particular, the chatbot determination module 420 may include anatural language input/output module 421, a context generation module422, a domain determination module 423, and a chatbot connectionmanagement module 424. The natural language input/output module 421 candisplay natural language processing on the text information of theinputted user voice on the chat screen. The context generation module422 may generate the context information necessary to determine thechatbot providing the response message. The context information mayinclude user profile information, user search information, userpreference information, and the like, as well as chat historyinformation on the chat screen. The domain determination module 423 candetermine the domain of the user voice input using the chatbotdetermination model 430. That is, the domain determination module 423can determine a chatbot that can provide a response message to theuser's voice. The chatbot connection management module 124 can managevarious information that can be connected to the chatbot server.

The chatbot determination model 430 may be a model that is learned todetermine a chatbot for providing a response message based on contextinformation including text information and chat history informationcorresponding to a user's voice. The chatbot determination model 430 canbe learned based on the functions included in the structure informationof the chatbot.

Through the chatbot registration module 410 and the chatbotdetermination module 420 as described above, a user can receive variousservices while maintaining the context by only talking with the chatbotwithout any additional input.

FIG. 5 is a flowchart describing a method for registering a chatbotaccording to an embodiment of the disclosure.

First, the electronic device 100 can detect an event for registering anew chatbot in operation S510. An event for registering a new chatbotmay include an event in which registration request information isreceived from a new chatbot, an event in which a user receives a userinput for registering a new chatbot, and the like, but the disclosure isnot limited thereto.

The electronic device 100 may determine whether the new chatbotstructure information can be grasped through the metadata of the newchatbot to be registered in operation S520. That is, the electronicdevice 100 can determine whether or not it is possible to graspinformation on attributes required for performing the functions andfunctions provided by the new chatbot through the metadata. Thestructure information of the new chatbot may be information thathierarchically stores information on the new chatbot, a function thatthe new chatbot can provide, and information on an attribute required toperform the function. In addition, although the metadata of the newchatbot may be a known format such as JSON, XML, etc., it may be aunique format for representing the metadata of the new chatbot.

For example, referring to FIG. 6A, when the new chatbot is a shoppingchatbot, the structure information of the shopping chatbot may beinformation that hierarchically stores information 610 about theshopping chatbot, a function 620 provided by the shopping chatbot, andthe sub-function 630 that can be provided by the shopping chatbot, andan attribute 640 necessary for performing the function or thesub-function.

If the structure information can be grasped through the metadata of thenew chatbot in operation S520 Y, the electronic device 100 can registerthe structure information of the new chatbot based on the metadata inoperation S530. As an example, the electronic device 100 may registerthe structure information of the new chatbot as shown in FIG. 6A in thepersonal assistant program.

If the structure information cannot be grasped through the metadata ofthe new chatbot in operation S520 N, the electronic device 100 canperform a dialogue with the new chatbot in operation S540. Specifically,the electronic device 100 may transmit an inquiry message inquiringabout the attributes required to perform functions and functionsprovided by the new chatbot to the chatbot server that provides the newchatbot. For example, as shown in panel (a) of FIG. 6B, the electronicdevice 100 can transmit an inquiry message “What is a function of thechatbot?” To the IoT chatbot server to inquire about the function of thenew chatbot, and can receive a response message including information ona chatbot provided by the IoT chatbot server and information on afunction that the chatbot can provide. In order to check again thesub-functions provided by the IoT chatbot, the electronic device 100 maytransmit a message inquiring “What is a function of the chatbot?” to theIoT chatbot server, and receive from the IoT chatbot server a responsemessage including information on the chatbot provided by the IoT chatbotserver and functions that the chatbot can provide. Also, the electronicdevice 100 can transmit an inquiry message “Perform IoT search” in orderto inquire information about attributes necessary for performing afunction, and receive a response message “please tell types and vendors”including information on attributes necessary for IoT search from theIoT chatbot server. In the meantime, the electronic device 100 may notdisplay a dialogue as shown in panel (a) of FIG. 6B, but this is merelyexemplary, and a dialogue as in panel (a) of FIG. 6B for registeringstructure information according to user setting can be displayed on ascreen.

The electronic device 100 can grasp the structure information of the newchatbot through the dialog analysis in operation S550. Specifically, theelectronic device 100 can grasp the functions provided by the chatbotthrough the response message to the inquiry message inquiring about thefunctions provided by the new chatbot In addition, the electronic device100 can acquire information on attributes necessary for performing afunction through a response message to an inquiry message inquiringabout attributes necessary for performing a function. For example, theelectronic device 100 can register the structure information of the IoTchatbot as shown in panel (b) of FIG. 6B through a dialogue as shown inpanel (a) of FIG. 6B. That is, the information about the IoT chatbot650, the function 660 provided by the IoT chatbot, and the attributes670 necessary for performing the function can be grasped as thestructure information of the IoT chatbot.

The electronic device 100 may register the structure informationobtained through the dialogue analysis in operation S560. For example,the structure information of the chatbot as illustrated in panel (b) ofFIG. 6B can be registered to the personal assistant program.

In the aforementioned embodiment, it has been described that theelectronic device 100 registers the new chatbot and the structureinformation of the new chatbot, but this is merely exemplary, and theelectronic device 100 may update the structure information provided bythe existing chatbot by the aforementioned method.

FIG. 7 is a flowchart to describe an embodiment of performing chattingby changing a chatbot according to an embodiment of the disclosure.

First, the electronic device 100 receives a user voice uttered by theuser in operation S710. Specifically, the electronic device 100 mayreceive a user's voice through a microphone included in the userinputter 130. The personal assistant program is executed in theelectronic device 100, and a chat screen for chatting with the personalassistant chatbot can be displayed.

The electronic device 100 may analyze the user's voice in operationS720. Specifically, the electronic device 100 can acquire textinformation corresponding to the user's voice through natural languageprocessing on the user's voice. The electronic device 100 can acquireall the text information corresponding to the user's voice, but it isonly an embodiment and can extract the core text among the textinformation corresponding to the user's voice.

The electronic device 100 may determine whether the personal assistantchatbot is answerable based on the obtained text information inoperation S730. The electronic device 100 may determine whether theobtained text information is text information to perform a function ofthe electronic device 100 or to search for information stored in theelectronic device 100 and determine whether the obtained textinformation is the information that the personal assistant chatbot cananswer.

If the personal assistant chatbot is available in operation S730 Y, theelectronic device 100 may generate a response message via the personalassistant chatbot in operation S770. For example, when the textinformation corresponding to the user's voice is text information forinquiring the schedule stored in the electronic device 100 or forchanging the setting of the electronic device 100, the electronic device100 may generate a response message regarding the text informationthrough the personal assistant chatbot.

If the personal assistant chatbot is unavailable in operation S730 N,the electronic device 100 may determine a chatbot to provide a responsemessage using the learned module in operation S740. The learned modulemay be a chatbot determination module that is learned to determine achatbot for providing a response message using text information and chathistory information as input data.

Also, the electronic device 100 can determine one of the functionscorresponding to the chatbots determined based on the structureinformation. That is, the electronic device 100 may use the structureinformation of the chatbot stored in the personal assistant program todetermine the function of the chatbot that the user desires to inquire,and may generate an inquiry message corresponding to the determinedfunction. Specifically, the electronic device 100 may load structureinformation of the determined chatbot, processes the text informationcorresponding to the user's voice in a form that the chatbot can processbased on the structural information, and process the context informationincluding the chat history information to the chatbot in a processableform to generate an inquiry message. For example, if what the userwishes to inquire about is an article purchasing function of thechatbot, the electronic device 100 may generate an inquiry message so asto correspond to the article purchasing function based on the structureinformation of the shopping chatbot.

The electronic device 100 may transmit the text information and the chathistory information corresponding to the utterance to the chat serverprovided with the determined chat bots in operation S750. At this time,the electronic device 100 may transmit not only chat history informationbut also context information such as user profile information, usersearch information, and user preference information. An informationtransmission standard for transmitting text information and chat historyinformation may exist between the personal assistant program and thechatbot stored in the electronic device 100, and the electronic device100 may transmit an inquiry to the electronic device 100 using theinformation transmission standard.

The electronic device 100 may receive a response message from thechatbot server in operation S760. The chatbot server may transmit aresponse message using an inquiry message corresponding to a functiondetermined ahead.

The electronic device 100 may display a response message which isgenerated through the personal assistant chatbot or received from thechatbot server in operation S780.

FIGS. 8, 9A, and 9B are views to describe embodiments of transmitting aninquiry message and receiving a response message with a chatbot serveraccording to an embodiment of the disclosure.

Referring to FIG. 8, a user can chat with a personal assistant chatbotand a shopping chatbot. That is, when the user inputs a user voice forshopping, the electronic device 100 may receive an inquiry message 810for shopping, and the electronic device 100 may send an inquiry message820 to a user through the personal assistant chatbot, and may receivethe request message 830 again from the user. The electronic device 100may transmit the inquiry message 810 to the shopping chatbot serverdetermined in response to the request message 830 received from the userand receive the response message 840 from the shopping chatbot server.

In particular, the electronic device 100 and the shopping chatbot servermay go through the process as described in FIG. 9A and transceive theinquiry message 810 and the response message 840.

FIG. 9A is a diagram to describe an embodiment to generate an inquirymessage through natural language processing and receive a responsemessage.

Referring to FIG. 9A, when a user request including a response messageis received in operation S845, the personal assistant program of theelectronic device 100 may perform processing request to the shoppingchatbot in operation S850.

The shopping chatbot server may transmit a reply to a personal assistantprogram in response to a request for processing in operation S860. Thereply may include information on the shopping chatbot provided by theshopping chatbot server and information on a function provided by theshopping chatbot.

The personal assistant program of the electronic device 100 may transmita processing request in operation S870 including “Galaxy price inquiryand context information” for searching a product to the shopping chatbotserver based on the received reply.

The shopping chatbot server may send a reply including the responsemessage 840 to the electronic device 100 in response to the processingrequest in operation S880, and the personal assistant program may sendthe received reply to the user as shown in FIG. 8 in operation S885.

FIG. 9B is a view for describing an embodiment of generating andtransmitting an inquiry message and receiving a response message usingthe structure information and the information transmission standard.

Referring to FIG. 9B, the structure information of the shopping chatbotis registered in the personal assistant program of the electronic device100, and the information transmission standard according to thestructure information may exist between the personal assistant programof the electronic device 100 and the shopping chatbot.

To be specific, the electronic device 100 and the shopping chatbotserver may go through the process of FIG. 9B and transceive the inquirymessage 810 and the response message 840.

First, when a user request including a response message is received inoperation S910, the personal assistant program of the electronic device100 can perform a processing request to the shopping chatbot inoperation S920. The processing request may include an inquiry messagegenerated according to the structure information. That is, theprocessing request may include the inquiry message that is generatedaccording to a function that can be provided by the shopping chatbotbased on the structure information of the shopping chatbot and theattribute to perform the function. The inquiry message may follow theinformation transmission standard that is determined between thepersonal assistant program and the shopping chatbot.

The shopping chatbot server may transmit a response, in response to theprocessing request in operation S930. The shopping chatbot server mayalso generate a response message according to the method determined bythe information transmission standard and transmit the response messageto the personal assistant program of the electronic device 100. Inparticular, the response message generated according to the informationtransmission standard may have a structure such as a text and a selectoras shown in FIG. 9B. The text may include a reply to the user inquiry,and the selector may include an indicator that can be selected toperform the user inquiry.

The personal assistant program may process the received response messageto be displayed on a screen and provide the same to a user in operationS940.

As described above, by transmitting an inquiry message and a responsemessage based on the structure information and the informationtransmission standard, a response message can be received more rapidlyand efficiently without an unnecessary process.

FIG. 10 is a flowchart to describe a controlling method of an electronicdevice according to an embodiment of the disclosure.

Referring to FIG. 10, the electronic device 100 may receive a user'svoice uttered by a user in operation S1010.

The electronic device 100 can acquire text information from the user'svoice and display the text information on the chat screen in operationS1020. The electronic device 100 can acquire the text informationthrough the user's voice uttered by the user, but this is merely anexample, and the text information can be acquired according to a userinput that is input through a keypad displayed on the display and a userinput that is input through an external device (e.g., a keyboard).

The electronic device 100 can determine the chatbot by inputting thetext information and the chat history information into the learned modelin operation S1030. The learned model may be a chatbot determinationmodel that is learned to determine a chatbot that can provide a responsemessage to user voice using text information and chat historyinformation as input data.

The electronic device 100 may transmit the text information and chathistory information to the chatbot server providing the determinedchatbot in operation S1040. The electronic device 100 may transmitcontext information such as user profile information, user searchinformation, user preference information, and the like, in addition tothe chat history information. In addition, the electronic device 100 maygenerate an inquiry message including text information and chat historyinformation according to a predetermined information transmissionstandard with the chat server, and may transmit the inquiry message tothe chat server.

The electronic device 100 may receive a response message from thechatbot server in operation S1050. The determined chatbot server cangenerate a response message to the user voice based on the inquirymessage generated by the information transmission standard. The responsemessage generated by the chatbot may also be generated according to theinformation transmission standard between the electronic device 100 andthe chatbot server.

The electronic device 100 may display a response message in operationS1060. To be specific, the electronic device 100 may process theresponse message received from the chatbot server to be displayable andprovide the same to a user.

FIG. 11 is a view briefly illustrating a system including an electronicdevice, a server providing a personal assistant chatbot and an externalchatbot server according to another embodiment of the disclosure.

Referring to FIG. 11, the personal assistant program is stored in theelectronic device 100, However, the personal assistant program may bestored in a separate personal assistant chatbot server. That is, theelectronic device 100 can communicate with the external chatbot server200-1, 200-2, . . . 200-n using a personal assistant chatbot server1100.

Specifically, the personal assistant chatbot server 1100 may store apersonal assistant program capable of providing an AI service to a userof the electronic device 100. The personal assistant chatbot server 1100can provide various services (e.g., chat service, search service,reservation service, shopping service, etc.) to the electronic device100 using the personal assistant program.

The personal assistant chatbot server 1100 may store context information(e.g., user profile information, user search information, userpreference information, and the like) for the electronic device 100 andthe user of the electronic device 100. In addition, the personalassistant chatbot server 1100 may store chat history information on thechat screen. In addition, the personal assistant chatbot server 1100 maystore information on the chatbots provided by the external chatbotserver 200 (e.g., the structure information of the chatbot).

A specific method of communicating by the electronic device with theexternal chatbot server 200 using the personal assistant chatbot server1100 will be described with reference to FIG. 12.

FIG. 12 is a flowchart to describe an embodiment of chanting a chatbotby the personal assistant chatbot according to another embodiment of thedisclosure.

Referring to FIG. 12, the electronic device 100 may receive a user'svoice in operation S1210. The electronic device 100 may acquire textinformation corresponding to the input user's voice.

The electronic device 100 can display text information corresponding tothe user's voice in operation S1220. The electronic device 100 candisplay the text information on the chat screen for chatting with thepersonal assistant chatbot.

The electronic device 100 may transmit the text information to thepersonal assistant chatbot server 1100 in operation S1230.

The personal assistant chatbot server 1100 can determine a chatbot thatcan provide a response regarding the user's voice using the learnedmodel in operation S1240. Specifically, the personal assistant chatbotserver 1100 may provide a response message regarding the user's voiceusing a chatbot determination model that is learned to determine thechatbot for providing a response message by using the text informationand the chat history information as input data.

The personal assistant chatbot server 1100 can determine a chatbotprovided by an external chatbot server as a chatbot that can provide aresponse message, and can determine a personal assistant chatbot as achatbot that can provide a response message.

The personal assistant chatbot server 1100 may transmit an inquirymessage to the electronic device 100 in operation S1245. The inquirymessage may be a message inquiring whether to transmit the responsemessage to the external chatbot server.

The electronic device 100 may transmit the request message in responseto the inquiry message to the personal assistant chatbot server 1100 inoperation S1247.

The personal assistant chatbot server 1100 may transmit the textinformation and the chat history information to the chatbot server 200providing the determined chatbot in response to the request message inoperation S1250. The personal assistant chatbot server 1100 can generatean inquiry message including text information and chat historyinformation based on the structure information of the chatbot registeredin the personal assistant chatbot server 1100, and transmit the inquirymessage to the external chatbot server 200. The inquiry messagegenerated herein may be an inquiry message generated according to theinformation transmission standard existing between the personalassistant chatbot server 1100 and the external chatbot server 200. Inaddition, the personal assistant chatbot server 1100 can transmit thecontext information together with the text information and the chathistory information to the external chat server 200.

The external chatbot server 200 may generate a response message inoperation S1260. The response message is a message to provide a reply tothe inquiry message and may be generated according to the informationtransmission standard that exists between the personal assistant chatbotserver 1100 and the external chatbot server 200.

The external chatbot server 200 may transmit the generated responsemessage to the personal assistant chatbot server 1100 in operation inoperation S1270 and the personal assistant chatbot server 1100 maytransmit the response message to the electronic device 100 in operationS1280. T, the personal assistant chatbot server 1100 can update the chathistory information by storing the response message and also display theresponse message in operation S1290.

The electronic device 100 may display a response message on a chatscreen for performing chatting with the personal assistant chatbot.

FIG. 13 is a block diagram illustrating a configuration of an electronicdevice to learn and use a model for determining a chatbot according toan embodiment of the disclosure.

Referring to FIG. 13, a processor 1300 may include at least one of alearning unit 1310 and a determination unit 1320. The processor 1300 ofFIG. 13 may correspond to the processor 150 of the electronic device 100or the processor of the data learning server (not shown).

The learning unit 1310 may generate or train a determination modelhaving a criteria for determining a chatbot for providing a responsemessage using learning data. The learning unit 1310 may generate adetermination model having a determination criteria using the collectedlearning data.

For example, the learning unit 1310 can determine the domain of thechatbot using the function and attribute information of the registeredexternal chatbot as learning data. The learning unit 1310 may generate,learn, or update a chatbot determination model for providing a responsemessage using text information and chat history information as learningdata based on the domain of the chatbot.

The determination unit 1320 may estimate a chatbot providing a responsemessage regarding a predetermined user's voice using the predetermineddata as input data for the learned determination model.

As one example, the determination unit 1320 may determine (or estimate,deduct) a chatbot providing a response message for user's voice by usingthe text information and chat history information as input data for thelearned chatbot determination model.

At least a portion of the learning unit 1310 and at least a portion ofthe determination unit 1320 may be implemented in a software module orin the form of at least one hardware chip and mounted in an electronicdevice. For example, at least one of the learning unit 1310 and thedetermination unit 1320 may be fabricated in the form of a dedicatedhardware chip for artificial intelligence (AI), or a general-purposeprocessor (e.g., a CPU or an application processor or a graphics-onlyprocessor (e.g., a GPU) and may be mounted on various electronic devicesas described above. The dedicated hardware chip for artificialintelligence is dedicated processor for probability calculation, and ithas higher parallel processing performance than existing general purposeprocessor, so it can quickly process computation tasks in artificialintelligence such as machine learning. When the learning unit 1310 andthe determination unit 1320 are implemented with a software module (or aprogram module including an instruction), the software module may be anon-transitory computer readable media. In this case, the softwaremodule may be provided by an operating system (OS) or by a predeterminedapplication. Alternatively, some of the software modules may be providedby an OS, and some of the software modules may be provided by apredetermined application.

In this case, the learning unit 1310 and the determination unit 1320 maybe mounted on one electronic device or on separate electronic devices,respectively. For example, one of the learning unit 1310 and thedetermination unit 1320 may be included in the electronic device 100,and the other one may be included in an external server. The learningunit 1310 and the determination unit 1320 may provide the modelinformation constructed by the learning unit 1310 to the determinationunit 1320 via the wired or wireless communication system, and data whichis input to the determination unit 1320 may be provided to the learningunit 1310 as additional learning data.

FIG. 14A is a block diagram illustrating a learning unit and adetermination unit according to various embodiments of the disclosure.

Referring to FIG. 14A, in panel (a) the learning unit 1310 according tosome embodiments may include a learning data acquisition unit 1310-1 anda model learning unit 1310-4. The learning unit 1310 may further includeat least one of the learning data preprocessor 1310-2, the learning dataselection unit 1310-3, and the model evaluation unit 1310-5 selectively.

The learning data acquisition unit 1310-1 can acquire learning datanecessary for a determination model for determining a chatbot. In theembodiment of the disclosure, the learning data acquisition unit 1310-1can acquire text information and chat history information (or contextinformation) as learning data. Further, the learning data acquisitionunit 1310-1 can acquire, as learning data, functions provided by thechatbot and attribute information necessary for performing functions.The learning data may be data collected or tested by the learning unit1310 or the manufacturer of the learning unit 1310.

The model learning unit 1310-4 can use the learning data to train thatthe determination model has a criterion of how to determine a chatbotthat can provide a response message regarding the user's voice. Forexample, the model learning unit 1310-4 can train a determination modelthrough supervised learning using at least some of the learning data asa determination reference. Alternatively, the model learning unit 1310-4may, for example, train the determination model through unsupervisedlearning for finding out a determination criteria for determining asituation by learning itself using learning data without specificsupervision. Further, the model learning unit 1310-4 may, for example,may train the determination model through a reinforcement learning whichuses feedback regarding whether a result of determination of a situationaccording to learning is correct. In addition, the model learning unit1310-4 may train the determination model using learning algorithm and soon including error back-propagation or gradient descent.

The model learning unit 1310-4 may learn the selection criteriaregarding which learning data needs to be used in order to determine achatbot providing a response message using the input data.

The model learning unit 1310-4 can determine a determination modelhaving a large relevance between the input learning data and the basiclearning data as a determination model to be learned when a plurality ofdetermination models constructed in advance exist. In this case, thebasic learning data may be pre-classified according to the data type,and the determination model may be pre-built for each data type. Forexample, the basic learning data may be pre-classified by variouscriteria such as an area where the learning data is generated, a time atwhich the learning data is generated, a size of the learning data, agenre of the learning data, a creator of the learning data, a type ofobjects within learning data, and so on.

When the determination model is learned, the model learning unit 1310-4can store the learned determination model. In this case, the modellearning unit 1310-4 can store the learned determination model in thememory 140 of the electronic device 100. Alternatively, the modellearning unit 1310-4 may store the learned determination model in thememory of the electronic device 100 and a server (e.g., a personalassistant chatbot server 1100) connected via a wired or wirelessnetwork.

The learning unit 1310 may further include the learning datapreprocessor 1310-2 and the learning data selection unit 1310-3 in orderto improve the determination result of the determination model or tosave resources or time required for generation of a determination model.

The learning data preprocessor 1310-2 can preprocess the acquired data.so that the acquired data can be used for the learning for determiningthe chatbot. The learning data preprocessor 1310-2 can process theacquired data into a predetermined format so that the model learningunit 1310-4 can use the data acquired for learning for the chatbotdetermination. For example, the learning data preprocessor 1310-2 canremove text (e.g., adverbs, exclamations, etc.) that is not necessaryfor the determination model among the inputted text information.

The learning data selection unit 1310-3 can select data acquired by thelearning data acquisition unit 1310-1 or data necessary for learningfrom data preprocessed by the learning data preprocessor 1310-2. Theselected learning data may be provided to the model learning unit1310-4. The learning data selection unit 1310-3 can select the learningdata necessary for learning from the acquired or preprocessed dataaccording to a predetermined selection criterion. The learning dataselection unit 1310-3 may also select learning data according to apredetermined selection criterion by learning by the model learning unit1310-4. For example, the learning data selection unit 1310-3 can selectthe learning data to correspond to the structure information of thechatbot.

The learning unit 1310, in order to improve the determination result ofthe determination model, may further include the model evaluation unit1310-5.

The model evaluation unit 1310-5 inputs evaluation data to thedetermination model, and if the determination result outputted from theevaluation data does not satisfy the predetermined criterion, the modelevaluation unit 1310-5 can let the model learning unit 1310-4 learnagain. In this case, the evaluation data may be predefined data forevaluating the determination model.

For example, the model evaluation unit 1310-5 may determine that, if thenumber or ratio of evaluation data of which determination result is notcorrect exceeds a predetermined threshold value, from among thedetermination results of the determination model that is learned withrespect to the evaluation data, the predetermined criteria is notsatisfied.

When there are a plurality of learned determination models, the modelevaluation unit 1310-5 may evaluate whether each of the learneddetermination models satisfies a predetermined criterion, and determinea model satisfying a predetermined criterion as a final determinationmodel. In this case, when there are a plurality of models satisfying thepredetermined criterion, the model evaluation unit 1310-5 can determineany one or a predetermined number of models previously set in thedescending order of evaluation scores as a final determination model.

In panel (b) of FIG. 14A, the determination unit 1320 according to someembodiments may include the input data acquisition unit 1320-1 and thedetermination result providing unit 1320-4.

The determination unit 1320 may further include at least one of theinput data preprocessor 1320-2, input data selection unit 1320-3, andthe model update unit 1320-5.

The input data acquisition unit 1320-1 can acquire the data necessaryfor determining the chatbot providing the response message. As a resultof the determination, the determination result providing unit 1320-4 maydetermine the chatbot providing the response message by applying theinput data obtained from the input data acquisition unit 1320-1 to thelearned decision model. As a result of the determination, thedetermination result providing unit 1320-4 may apply the data selectedby the input data preprocessor 1320-2 or the input data selection unit1320-3, which will be described later. The determination result may bedetermined by a determination model.

As an embodiment, the determination result providing unit 1320-4 maydetermine (or estimate) a chatbot which provides a response message byapplying the text information and chat history information (e.g.,context information, etc.) obtained the input data acquisition unit1320-1 to the learned determination model.

The determination unit 1320 may further include the input datapreprocessor 1320-2 and the input data selection unit 1320-3 in order toimprove the determination result of the determination model or saveresources or time for providing the determination results.

The input data preprocessor 1320-2 can pre-process the acquired data sothat the acquired data can be used for determining the chatbot. Theinput data preprocessor 1320-2 can process the acquired data into apredefined format so that the determination result providing unit 1320-4can use the data acquired for the chatbot determination.

The input data selection unit 1320-3 can select the data acquired by theinput data acquisition unit 1320-1 or the data necessary for thesituation determination among the data preprocessed by the input datapreprocessor 1320-2. The selected data may be provided to thedetermination result providing unit 1320-4 as a determination result.The input data selection unit 1320-3 can select some or all of theobtained or preprocessed data according to a predetermined selectioncriterion for the situation determination. The input data selection unit1320-3 can also select data according to a predetermined selectioncriterion by learning by the model learning unit 1310-4.

The model update unit 1320-5 can control the determination model to beupdated based on the evaluation of the determination result provided bythe determination result providing unit 1320-4 as a determinationresult. For example, the model update unit 1320-5 may provide the modellearning unit 1310-4 with the determination result provided by thedetermination result providing unit 1320-4 as a determination result, sothat the model learning unit 1310-4 can additionally learn or update thedetermination model.

FIG. 14B is a view illustrating an example that an electronic device andan external server are interlocked with each other to learn anddetermine data according to an embodiment of the disclosure.

Referring to FIG. 14B, an external server (S) may learn the criteria fordetermining a chatbot for providing a response message, and theelectronic device 100 may determine a situation based on a learningresult by the external server (S).

In this case, the model learning unit 1310-4 of the external server (S)can perform the function of the learning unit 1310 shown in FIG. 13. Themodel learning unit 1310-4 of the external server (S) may learn criteriaregarding which text information or context information to use in orderto determine a chatbot for providing the response message or how todetermine the chatbot for providing a response message using theinformation.

The determination result providing unit 1320-4 of the electronic device100 may determine a chatbot for providing a response message by applyingthe data selected by the input data selection unit 1320-3 to thedetermination model generated by the server (S). Alternatively, thedetermination result providing unit 1320-4 of the electronic device 100may receive the determination model generated by the server 50 from theserver 50, and determine a chatbot for providing a response messageusing the received determination model.

FIG. 15 is a flowchart of network system using a chatbot determinationmodel according to various embodiments of the disclosure.

Referring to FIG. 15, the network system using the determination modelmay include a first component 1501 and a second component 1502.

The first component 1501 may be the electronic device 100 and the secondcomponent 1502 may be the server (S) storing the determination model.Alternatively, the first component 1501 may be a general purposeprocessor and the second component 1502 may be an artificialintelligence dedicated processor. Alternatively, the first component1501 may be at least one application, and the second component 1502 maybe an operating system (OS). That is, the second component 1502 may be acomponent which is more integrated, dedicated, has less delay, hashigher performance, or has greater resources than the first component1501, and can rapidly and effectively process greater computations whichare required for generation, update or application of the determinationmodel compared to the first component 1501.

In this case, an interface for transmitting/receiving data between thefirst component 1501 and the second component 1502 may be defined. Forexample, an application program interface (API) having learning data tobe applied to a determination model as an argument value (or anintermediate value or a transfer value) may be defined. The API is a setof subroutines or functions that can be called for any processing ofanother protocol (e.g., a protocol defined in server (S)) in any oneprotocol (e.g., a protocol defined in electronic device 100). That is,an environment in which an operation of another protocol can beperformed through any one protocol through an API can be provided.

The third component 1503 may be implemented as a chatbot for providing aresponse message or a chatbot stored in the electronic device 100.

In FIG. 15, the first component 1501 may display a chat screen inoperation S1505. The chat screen can be a screen in which chat isperformed with a personal assistant chatbot or another chatbot.

While the chat screen is being displayed, the first component 1501 mayreceive a user's voice in operation S1510. The first component 1501 mayprocess a user's voice and acquire text information corresponding to theuser's voice.

The first component 1501 may display the text information correspondingto the user's voice in operation S1520.

The first component 1501 may transmit the text information to the secondcomponent 1502 in operation S1530. At this time, the first component1501 may transmit various context information (e.g., user profileinformation, user search information, user preference information, etc.)together to the second component.

The second component 1502 may determine a chatbot capable of providing aresponse message based on the received text information in operationS1540. Specifically, the second component 1502 can determine the chatbotthat provides the response message to the user voice by inputting thecontext information including the text information and the chat historyinformation into the learned determination model. The learneddetermination model can be learned to determine a chatbot that providesa response message using text information and context information asinput data.

The second component 1502 may generate the inquiry message in operationS1550. The second component 1502 can determine the functioncorresponding to the user's voice based on the structure information ofthe determined chatbot. The second component 1502 may then generate aninquiry message containing text information and context information toperform the determined function. In addition, the second component 1502can generate an inquiry message according to the informationtransmission specification between the second component 1502 and a thirdcomponent 1503.

The second component 1502 may transmit the inquiry message to the thirdcomponent 1503 in operation S1560, and the third component 1503 maygenerate the response message based on the inquiry message in operationS1570. The third component 1503 may generate a response messageaccording to the information transmission standard between the secondcomponent 1502 and the third component 1503.

The third component 1503 may transmit a response message to the secondcomponent 1502 in operation S1580, and the second component 1502 maytransmit the received response message to the first component 1501 againin operation S1590.

The first component 1501 may display a response message on a chat screenin operation S1595.

According to various embodiments of the disclosure as described above, auser can reduce the irritating task that a user has to specify achatbot, and can perform conversation with a changed chatbot withoutinterruption of the conversation even if the chatbot is changed.

Various embodiments of the disclosure may be implemented as softwareincluding instructions included in the machine (e.g.: computer) readablestorage media. The device calls instructions stored in the storagemedium and is operable according to the called instructions, and mayinclude an electronic device (e.g.: electronic device 100) according tothe disclosed various embodiments. If the instructions are implementedby a processor, the processor may perform a function corresponding tothe instructions by the processor itself or using other components underthe control of the processor. The instructions may include a codegenerated or executed by a compiler or interpreter. A machine-readablestorage medium can be provided in a format of a non-transitory storagemedium. The “non-transitory” indicates that the storage medium does notinclude a signal and is tangible, but does not distinguish that the datais stored in the storage medium semi-permanently or temporarily.

According to an embodiment, the method according to various embodimentsdisclosed herein may be provided in a computer program product. Acomputer program product may be traded between a seller and a purchaseras a commodity. A computer program product may be distributed in theform of a machine-readable storage medium (e.g.: compact disc read onlymemory (CD-ROM)) or distributed online through an application store(e.g.: PlayStore™). In the case of on-line distribution, at least aportion of the computer program product may be stored temporarily or atleast temporarily in a storage medium such as a manufacturer's server, aserver of an application store, or a memory of a relay server.

Each of the components (e.g., module or program) according to variousembodiments may be comprised of a single entity or a plurality ofentities, and some subcomponents of the previously mentionedsubcomponents may be omitted, or other subcomponents May be furtherincluded in various embodiments. Alternatively or additionally, somecomponents (e.g., modules or programs) may be integrated into one entityto perform the same or similar functions performed by each respectivecomponent prior to integration. Operations performed by a module,program, or other component, in accordance with various embodiments, maybe performed sequentially, in a parallel, repetitive, or heuristicallymanner, or at least some operations may be performed in a differentorder.

The non-transitory computer readable medium refers to a medium thatstores data semi-permanently, and is readable by an apparatus.Specifically, the above-described various applications or programs maybe stored in the non-transitory computer readable medium such as acompact disc (CD), a digital versatile disc (DVD), a hard disk, aBlu-ray disk, a universal serial bus (USB), a memory card, a ROM oretc., and may be provided.

While the disclosure has been shown and described with reference tovarious embodiments thereof, it will be understood by those skilled inthe art that various changes in form and details may be made thereinwithout departing from the spirit and scope of the disclosure as definedby the appended claims and their equivalents.

What is claimed is:
 1. A controlling method of an electronic device fordetermining a chatbot using an artificial intelligence learning model,the method comprising: receiving a voice uttered by a user; processingthe voice and acquiring text information corresponding to the voice, anddisplaying the text information on a chat screen; determining a chatbotfor providing a response message regarding the voice by inputting theacquired text information and chat history information regarding thechat screen to a model which is trained to determine the chatbot byinputting text information and chat history information; transmittingthe acquired text information and the chat history information regardingthe chat screen to a server for providing the determined chatbot; andreceiving a response message from the server and displaying the responsemessage on the chat screen.
 2. The controlling method as claimed inclaim 1, the method further comprising: in response to an event forregistering a new chatbot occurring, acquiring structure information ofthe new chatbot; and registering the new chatbot and the structureinformation together.
 3. The controlling method as claimed in claim 2,wherein the acquiring of the structure information comprises: inresponse to the structure information not being identified through metainformation of the new chatbot, transmitting an inquiry message forinquiring structure information of the new chatbot to a server forproviding the new chatbot; receiving a response message regarding theinquiry message; and generating structure information of the new chatbotbased on the response message.
 4. The controlling method as claimed inclaim 1, wherein the determining of the chatbot comprises determining achatbot corresponding to the user input by inputting the acquired textinformation and chat history information regarding the chat screen to alearned model and determining one of functions provided by the chatbotbased on structure information of the determined chatbot.
 5. Thecontrolling method as claimed in claim 1, wherein the chat screen is ascreen for chatting with a personal assistant chatbot, and wherein themethod comprises: determining whether the acquired text information istext information which the personal assistant chatbot can respond to bydetermining whether the acquired text information is text informationfor performing a function within the electronic device or for searchinginformation stored in the electronic device.
 6. The controlling methodas claimed in claim 5, further comprising: in response to the acquiredtext information being text information that the personal assistantchatbot can respond to, displaying a response message regarding thevoice by the personal assistant chatbot.
 7. The controlling method asclaimed in claim 5, wherein the determining of the chatbot comprises, inresponse to the acquired text information being text information thepersonal assistant chatbot cannot respond, determining a chatbotcorresponding to the user input by inputting the acquired textinformation and the chat history information to a learned model.
 8. Thecontrolling method as claimed in claim 6, wherein the transmitting ofthe acquired text information and the chat history information comprisestransmitting the text information and the chat history information basedon information transmission standards between a server providing thepersonal assistant chatbot and a server providing the determinedchatbot.
 9. The controlling method as claimed in claim 1, wherein thedisplaying of the response message comprises displaying the responsemessage along with an icon corresponding to the determined chatbot. 10.The controlling method as claimed in claim 1, wherein the transmittingof the acquired text information and the chat history informationcomprises transmitting profile information of the user and searchinformation of the user along with the acquired text information and thechat history information regarding the chat screen to a server providingthe determined chatbot.
 11. An electronic device comprising: a display;a user inputter; a communicator; at least one processor electricallyconnected to the display, the user inputter, and the communicator; and amemory electrically connected to the at least one processor, wherein theat least one processor is configured to: control the display to displaya chat screen, when voice uttered by a user is input through the userinputter, acquire text information corresponding to the voice byprocessing the voice, control the display to display the textinformation on the chat screen, determine a chatbot for providing aresponse message regarding the voice by inputting the acquired textinformation and chat history information regarding the chat screen to amodel which is trained to determine the chatbot by inputting textinformation and chat history information, control the communicator totransmit the acquired text information and the chat history informationregarding the chat screen to a server for providing the determinedchatbot, and control the display to receive a response message from theserver and display the response message on the chat screen.
 12. Theelectronic device as claimed in claim 11, wherein the at least oneprocessor, in response to an event to register a new chatbot occurring,is further configured to acquire structure information of the newchatbot and registers the structure information along with the newchatbot.
 13. The electronic device as claimed in claim 12, wherein theat least one processor, in response to the structure information notbeing identified through metadata of the new chatbot, is furtherconfigured to: control the communicator to transmit an inquiry messagefor inquiring the structure information of the new chatbot to a serverproviding the new chatbot, receive a response message regarding theinquiry message through the communicator, and generate structureinformation of the new chatbot based on the response message.
 14. Theelectronic device as claimed in claim 12, wherein the at least oneprocessor is further configured to: determine a chatbot corresponding tothe user input by inputting the acquired text information and the chathistory information regarding the chat screen to a learned model, anddetermine one of functions provided by the determined chatbot based onthe structure information of the determined chatbot.
 15. The electronicdevice as claimed in claim 11, wherein the chat screen is a screen forchatting with a personal assistant chatbot, and wherein the at least oneprocessor is further configured to: determine whether the acquired textinformation is text information to perform a function within theelectronic device or to search for information stored in the electronicdevice, and determine whether the acquired text information is textinformation that the personal assistant chatbot can respond to.
 16. Theelectronic device as claimed in claim 15, wherein the at least oneprocessor, in response to the acquired text information being textinformation that the personal assistant chatbot is capable of respondingto, is further configured to control the display to display a responsemessage regarding the voice by the personal assistant chatbot.
 17. Theelectronic device as claimed in claim 15, wherein the at least oneprocessor, in response to the acquired text information being textinformation that the personal assistant chatbot is not capable ofresponding to, is further configured to input the acquired textinformation and the chat history information to a learned model todetermine a chatbot corresponding to the user input.
 18. The electronicdevice as claimed in claim 16, wherein the at least one processor isfurther configured to control the communicator to transmit the textinformation and the chat history information based on the informationtransmission standard between the server providing the personalassistant chatbot and the server providing the determined chatbot. 19.The electronic device as claimed in claim 11, wherein the at least oneprocessor is further configured to control the display to display theresponse message along with an icon corresponding to the determinedchatbot.
 20. The electronic device as claimed in claim 11, wherein theat least one processor is further configured to transmit profileinformation of the user and search information of the user along withthe acquired text information and chat history information regarding thechat screen to a server providing the determined chatbot.